This invention relates to the field of communication systems, and in particular, efficient maximum-likelihood (ML) detection for multiple-input multiple-output (MIMO) communication systems.
Wireless, as well as wired, communication systems may achieve high spectral efficiency by transmitting information over multiple antennas at the same time (i.e. simultaneously), or at about the same time, using the same frequency band. These systems are known as multiple-input multiple-output, or MIMO, systems.
One problem in designing MIMO systems is demodulating the received signals to recover transmitted bits. Typically, each received signal contains components of all transmitted signals, making demodulation more complicated than in a single-input single-output (SISO) system. To address this, many receivers employ linear equalization to separate the received signals. This approach is attractive for its low computational complexity, but it introduces a substantial performance penalty, resulting in significantly reduced data rates or ranges compared to what may otherwise be possible.
For better performance, ML detection may be used. Given a MIMO system described by r=Hx+n, where r is the received signal vector, H is the known (or estimated) channel matrix and n is additive noise, an ML detector searches over all possible transmit symbol vectors x to find the vector x which minimizes ∥r−Hx∥2. In a system with Nt spatial streams, each employing an M2-point constellation, this requires calculating M2Nt distance metrics. For example, in a system with two transmitted spatial streams, both transmitting 64-QAM QAM=quadrature amplitude modulation) constellations, this method would require calculating 642=4096 distance metrics. This complexity is too costly and power-intensive for many applications.
To reduce the cost and power consumption, some systems employ sub-optimal ML schemes, such as sphere decoding. These schemes seek to reduce the amount of computation by reducing the size of the search space. Unfortunately, in exchange for reduced complexity, these schemes may sacrifice performance and/or exhibit high variance in computation times, making such schemes undesirable for many applications.
In view of the above, there is a need for improved detection techniques for MIMO systems.